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   "source": [
    "# Estimating The Infected Population From Deaths\n",
    "> Estimating the number of infected people by country based on the number of deaths and case fatality rate. \n",
    "\n",
    "- comments: true\n",
    "- author: Joao B. Duarte\n",
    "- categories: [growth, compare, interactive, estimation]\n",
    "- hide: false\n",
    "- image: images/covid-estimate-infections.png\n",
    "- permalink: /covid-infected/\n",
    "- toc: true"
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   "source": [
    "> Warning: This analysis contains the results of a predictive model. There are a number of assumptions made which include some speculation.  Furthermore, this analysis was not prepared or reviewed by an Epidimiologist. Therefore, the assumptions and methods presented should be scrutinized carefully before arriving at any conclusions. "
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   "source": [
    "#hide\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import altair as alt\n",
    "from altair_saver import save\n",
    "#alt.renderers.enable('altair_saver', ['vega-lite', 'png'])\n",
    "from datetime import timedelta, datetime, date\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "chart_width = 550\n",
    "chart_height= 400"
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   "source": [
    "#hide\n",
    "def plot(data, type1, levels):\n",
    "    data_countries_pc2 = data.copy()\n",
    "    for i in range(0,len(countries)):\n",
    "        data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "        data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "        if type1 == \"scatter\":\n",
    "            data_countries_pc2[i]['cases'] = data_countries_pc2[i][\"total_cases\"]\n",
    "        data_countries_pc2[i]['infected'] = data_countries_pc2[i][\"total_infected\"]\n",
    "    data_plot = data_countries_pc2[0]\n",
    "    for i in range(1, len(countries)):    \n",
    "        data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "    \n",
    "    if type1 == \"scatter\":\n",
    "        data_plot[\"45_line\"] = data_plot[\"cases\"]\n",
    "\n",
    "    # Plot it using Altair\n",
    "    source = data_plot\n",
    "    \n",
    "    if levels == True:\n",
    "        ylabel = \"Total\"\n",
    "    else :\n",
    "        ylabel = \"Per Million\"\n",
    "\n",
    "    scales = alt.selection_interval(bind='scales')\n",
    "    selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "    if type1 == \"line\": \n",
    "        base = alt.Chart(source, title =  \"Estimated Infected Population By Country\").encode(\n",
    "            x = alt.X('n_days:Q', title = \"Days since outbreak\"),\n",
    "            y = alt.Y(\"infected:Q\",title = ylabel),\n",
    "            color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                             scale=alt.Scale(scheme='tableau20'))\n",
    "        )\n",
    "        \n",
    "        shades = base.mark_area().encode(\n",
    "            x='n_days:Q',\n",
    "            y='total_infected_lower:Q',\n",
    "            y2='total_infected_upper:Q',\n",
    "            opacity = alt.condition(selection, alt.value(0.2), alt.value(0.05))\n",
    "        )\n",
    "    \n",
    "        lines = base.mark_line().encode(\n",
    "            opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    "        ).add_selection(\n",
    "            scales\n",
    "        ).add_selection(\n",
    "            selection\n",
    "        ).properties(\n",
    "            width=chart_width,\n",
    "            height=chart_height\n",
    "        )\n",
    "        return(\n",
    "        ( lines + shades)\n",
    "        .configure_title(fontSize=20)\n",
    "        .configure_axis(labelFontSize=15,titleFontSize=18)\n",
    "        )\n",
    "    \n",
    "    if levels == True:\n",
    "        ylabel = \"Infected\"\n",
    "        xlabel = \"Cases\"\n",
    "    else :\n",
    "        ylabel = \"Per Million Infected\"\n",
    "        xlabel = \"Per Million Cases\"\n",
    "        \n",
    "    if type1 == \"scatter\":\n",
    "        base = alt.Chart(source, title = \"COVID-19 Cases VS Infected\").encode(\n",
    "            x = alt.X('cases:Q', title = xlabel),\n",
    "            y = alt.Y(\"infected:Q\",title = ylabel),\n",
    "            color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                             scale=alt.Scale(scheme='tableau20')),\n",
    "            opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    "        )\n",
    "\n",
    "        \n",
    "        scatter = base.mark_point().add_selection(\n",
    "            scales\n",
    "        ).add_selection(\n",
    "            selection\n",
    "        ).properties(\n",
    "            width=chart_width,\n",
    "            height=chart_height\n",
    "        )\n",
    "\n",
    "        line_45 = alt.Chart(source).encode(\n",
    "            x = \"cases:Q\",\n",
    "            y = alt.Y(\"45_line:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"infected\"])))),\n",
    "        ).mark_line(color=\"grey\", strokeDash=[3,3])\n",
    "        \n",
    "        return(\n",
    "        (scatter + line_45)\n",
    "        .configure_title(fontSize=20)\n",
    "        .configure_axis(labelFontSize=15,titleFontSize=18)\n",
    "        )"
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   "source": [
    "#hide \n",
    "# Get data on deaths D_t\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\", \n",
    "                   error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_deaths\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "#hide\n",
    "# Get data and clean it\n",
    "data_cases = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", error_bad_lines=False)\n",
    "\n",
    "data_cases = data_cases.drop(columns=[\"Lat\", \"Long\"])\n",
    "data_cases = data_cases.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data_cases = pd.DataFrame(data_cases.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data_cases.reset_index(inplace=True)  \n",
    "data_cases = data_cases.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data_cases['date'] =pd.to_datetime(data_cases.date)\n",
    "data_cases = data_cases.sort_values(by = \"date\")\n",
    "data_cases.loc[data_cases.location == \"US\",\"location\"] = \"United States\"\n",
    "data_cases.loc[data_cases.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "# Add countries\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\", \"India\", \"Switzerland\", \"Canada\", \"Australia\", \n",
    "             \"Russia\", \"Belarus\", \"Ukraine\"]\n",
    "\n",
    "data_final = pd.merge(data,\n",
    "                 data_cases\n",
    "                 )\n",
    "data_final[\"CFR\"] = data_final[\"total_deaths\"]/data_final[\"total_cases\"]\n",
    "\n",
    "\n",
    "data_final[\"total_infected\"] = np.NaN\n",
    "data_final = data_final.sort_values(by = ['location', 'date'])\n",
    "data_final = data_final.reset_index(drop = True)\n",
    "\n",
    "\n",
    "for j in countries:\n",
    "    for i in data_final[\"date\"].unique()[0:-8]:\n",
    "        data_final.loc[(data_final.date == i) & (data_final.location == j), \"total_infected\"] = data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"total_deaths\"].iloc[0]/data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"CFR\"].iloc[0]\n",
    "        \n",
    "\n",
    "# Estimate growth rate of infected, g        \n",
    "data_final['infected_g'] = np.log(data_final['total_infected'])\n",
    "data_final['infected_g'] = data_final['infected_g'].diff() \n",
    "\n",
    "# Estimate number of infected given g\n",
    "today = data_final.date.iloc[-1]\n",
    "for j in countries:\n",
    "    for i in range(7,-1,-1):\n",
    "        data_final.loc[(data_final.location == j) & (data_final.date == today - timedelta(i)), \"total_infected\"] = data_final.loc[data_final.location == j, \"total_infected\"].iloc[-i-2]*(1+data_final.loc[data_final.location == j, \"infected_g\"][-12:-8].aggregate(func = \"mean\"))\n",
    "    \n",
    "\n",
    "\n",
    "# Upper Bound\n",
    "data_final[\"total_infected_upper\"] = np.NaN\n",
    "data_final = data_final.sort_values(by = ['location', 'date'])\n",
    "data_final = data_final.reset_index(drop = True)\n",
    "for j in countries:\n",
    "    for i in data_final[\"date\"].unique()[0:-8]:\n",
    "        data_final.loc[(data_final.date == i) & (data_final.location == j), \"total_infected_upper\"] = data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"total_deaths\"].iloc[0]/(data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"CFR\"].iloc[0]*0.7)\n",
    "# Estimate growth rate of infected, g        \n",
    "data_final['infected_g'] = np.log(data_final['total_infected_upper'])\n",
    "data_final['infected_g'] = data_final['infected_g'].diff() \n",
    "# Estimate number of infected given g \n",
    "today = data_final.date.iloc[-1]\n",
    "for j in countries:\n",
    "    for i in range(7,-1,-1):\n",
    "        data_final.loc[(data_final.location == j) & (data_final.date == today - timedelta(i)), \"total_infected_upper\"] = data_final.loc[data_final.location == j, \"total_infected_upper\"].iloc[-i-2]*(1+data_final.loc[data_final.location == j, \"infected_g\"][-12:-8].aggregate(func = \"mean\"))\n",
    "\n",
    "# Lower Bound\n",
    "data_final[\"total_infected_lower\"] = np.NaN\n",
    "data_final = data_final.sort_values(by = ['location', 'date'])\n",
    "data_final = data_final.reset_index(drop = True)\n",
    "for j in countries:\n",
    "    for i in data_final[\"date\"].unique()[0:-8]:\n",
    "        data_final.loc[(data_final.date == i) & (data_final.location == j), \"total_infected_lower\"] = data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"total_deaths\"].iloc[0]/(data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"CFR\"].iloc[0]*1.3)\n",
    "# Estimate growth rate of infected, g        \n",
    "data_final['infected_g'] = np.log(data_final['total_infected_lower'])\n",
    "data_final['infected_g'] = data_final['infected_g'].diff() \n",
    "# Estimate number of infected given g \n",
    "today = data_final.date.iloc[-1]\n",
    "for j in countries:\n",
    "    for i in range(7,-1,-1):\n",
    "        data_final.loc[(data_final.location == j) & (data_final.date == today - timedelta(i)), \"total_infected_lower\"] = data_final.loc[data_final.location == j, \"total_infected_lower\"].iloc[-i-2]*(1+data_final.loc[data_final.location == j, \"infected_g\"][-12:-8].aggregate(func = \"mean\"))\n",
    "data_final.loc[data_final.total_infected_lower < data_final.total_cases, \"total_infected_lower\"] = data_final.loc[data_final.total_infected_lower < data_final.total_cases, \"total_cases\"]\n",
    "\n",
    "\n",
    "data_pc = data_final[['location', 'date', 'total_infected', 'total_infected_lower', 'total_infected_upper']].copy()\n",
    "\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\"]\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_infected\"] = data_pc.loc[data_pc.location == i,\"total_infected\"]\n",
    "\n",
    "# Get each country time series\n",
    "filter1 = data_pc[\"total_infected\"] > 1\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])      "
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   "source": [
    "## Estimated Infected Population By Country\n",
    "\n",
    "with respect to days since outbreak\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization. "
   ]
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\"n_days\": 61, \"infected\": 83505.0}, {\"index\": 10612, \"location\": \"Iran\", \"date\": \"2020-04-13T00:00:00\", \"total_infected\": 84802.0, \"total_infected_lower\": 73303.0, \"total_infected_upper\": 121145.71428571429, \"n_days\": 62, \"infected\": 84802.0}, {\"index\": 10613, \"location\": \"Iran\", \"date\": \"2020-04-14T00:00:00\", \"total_infected\": 85996.0, \"total_infected_lower\": 74877.0, \"total_infected_upper\": 122851.42857142858, \"n_days\": 63, \"infected\": 85996.0}, {\"index\": 10614, \"location\": \"Iran\", \"date\": \"2020-04-15T00:00:00\", \"total_infected\": 87026.0, \"total_infected_lower\": 76389.0, \"total_infected_upper\": 124322.85714285714, \"n_days\": 64, \"infected\": 87026.0}, {\"index\": 10615, \"location\": \"Iran\", \"date\": \"2020-04-16T00:00:00\", \"total_infected\": 88194.0, \"total_infected_lower\": 77995.0, \"total_infected_upper\": 125991.42857142858, \"n_days\": 65, \"infected\": 88194.0}, {\"index\": 10616, \"location\": \"Iran\", \"date\": \"2020-04-17T00:00:00\", \"total_infected\": 89328.00000000001, \"total_infected_lower\": 79494.0, \"total_infected_upper\": 127611.4285714286, \"n_days\": 66, \"infected\": 89328.00000000001}, {\"index\": 10617, \"location\": \"Iran\", \"date\": \"2020-04-18T00:00:00\", \"total_infected\": 90481.00000000001, \"total_infected_lower\": 80868.0, \"total_infected_upper\": 129258.57142857146, \"n_days\": 67, \"infected\": 90481.00000000001}, {\"index\": 10618, \"location\": \"Iran\", \"date\": \"2020-04-19T00:00:00\", \"total_infected\": 91472.0, \"total_infected_lower\": 82211.0, \"total_infected_upper\": 130674.28571428572, \"n_days\": 68, \"infected\": 91472.0}, {\"index\": 10619, \"location\": \"Iran\", \"date\": \"2020-04-20T00:00:00\", \"total_infected\": 92584.0, \"total_infected_lower\": 83505.0, \"total_infected_upper\": 132262.85714285716, \"n_days\": 69, \"infected\": 92584.0}, {\"index\": 10620, \"location\": \"Iran\", \"date\": \"2020-04-21T00:00:00\", \"total_infected\": 93657.0, \"total_infected_lower\": 84802.0, \"total_infected_upper\": 133795.7142857143, \"n_days\": 70, \"infected\": 93657.0}, {\"index\": 10621, \"location\": \"Iran\", \"date\": \"2020-04-22T00:00:00\", \"total_infected\": 94640.0, \"total_infected_lower\": 85996.0, \"total_infected_upper\": 135200.0, \"n_days\": 71, \"infected\": 94640.0}, {\"index\": 10622, \"location\": \"Iran\", \"date\": \"2020-04-23T00:00:00\", \"total_infected\": 95646.0, \"total_infected_lower\": 87026.0, \"total_infected_upper\": 136637.1428571429, \"n_days\": 72, \"infected\": 95646.0}, {\"index\": 10623, \"location\": \"Iran\", \"date\": \"2020-04-24T00:00:00\", \"total_infected\": 96448.0, \"total_infected_lower\": 88194.0, \"total_infected_upper\": 137782.85714285716, \"n_days\": 73, \"infected\": 96448.0}, {\"index\": 10624, \"location\": \"Iran\", \"date\": \"2020-04-25T00:00:00\", \"total_infected\": 97424.0, \"total_infected_lower\": 89328.0, \"total_infected_upper\": 139177.14285714287, \"n_days\": 74, \"infected\": 97424.0}, {\"index\": 10625, \"location\": \"Iran\", \"date\": \"2020-04-26T00:00:00\", \"total_infected\": 98647.0, \"total_infected_lower\": 90481.0, \"total_infected_upper\": 140924.28571428574, \"n_days\": 75, \"infected\": 98647.0}, {\"index\": 10626, \"location\": \"Iran\", \"date\": \"2020-04-27T00:00:00\", \"total_infected\": 99970.0, \"total_infected_lower\": 91472.0, \"total_infected_upper\": 142814.28571428574, \"n_days\": 76, \"infected\": 99970.0}, {\"index\": 10627, \"location\": \"Iran\", \"date\": \"2020-04-28T00:00:00\", \"total_infected\": 101650.0, \"total_infected_lower\": 92584.0, \"total_infected_upper\": 145214.28571428574, \"n_days\": 77, \"infected\": 101650.0}, {\"index\": 10628, \"location\": \"Iran\", \"date\": \"2020-04-29T00:00:00\", \"total_infected\": 103134.99999999999, \"total_infected_lower\": 93657.0, \"total_infected_upper\": 147335.7142857143, \"n_days\": 78, \"infected\": 103134.99999999999}, {\"index\": 10629, \"location\": \"Iran\", \"date\": \"2020-04-30T00:00:00\", \"total_infected\": 104691.0, \"total_infected_lower\": 94640.0, \"total_infected_upper\": 149558.57142857145, \"n_days\": 79, \"infected\": 104691.0}, {\"index\": 10630, \"location\": \"Iran\", \"date\": \"2020-05-01T00:00:00\", \"total_infected\": 106220.0, \"total_infected_lower\": 95646.0, \"total_infected_upper\": 151742.85714285716, \"n_days\": 80, \"infected\": 106220.0}, {\"index\": 10631, \"location\": \"Iran\", \"date\": \"2020-05-02T00:00:00\", \"total_infected\": 107603.0, \"total_infected_lower\": 96448.0, \"total_infected_upper\": 153718.57142857142, \"n_days\": 81, \"infected\": 107603.0}, {\"index\": 10632, \"location\": \"Iran\", \"date\": \"2020-05-03T00:00:00\", \"total_infected\": 109286.0, \"total_infected_lower\": 97424.0, \"total_infected_upper\": 156122.85714285716, \"n_days\": 82, \"infected\": 109286.0}, {\"index\": 10633, \"location\": \"Iran\", \"date\": \"2020-05-04T00:00:00\", \"total_infected\": 110767.0, \"total_infected_lower\": 98647.0, \"total_infected_upper\": 158238.57142857142, \"n_days\": 83, \"infected\": 110767.0}, {\"index\": 10634, \"location\": \"Iran\", \"date\": \"2020-05-05T00:00:00\", \"total_infected\": 112725.0, \"total_infected_lower\": 99970.0, \"total_infected_upper\": 161035.7142857143, \"n_days\": 84, \"infected\": 112725.0}, {\"index\": 10635, \"location\": \"Iran\", \"date\": \"2020-05-06T00:00:00\", \"total_infected\": 114533.0, \"total_infected_lower\": 101650.0, \"total_infected_upper\": 163618.57142857142, \"n_days\": 85, \"infected\": 114533.0}, {\"index\": 10636, \"location\": \"Iran\", \"date\": \"2020-05-07T00:00:00\", \"total_infected\": 116635.0, \"total_infected_lower\": 103135.0, \"total_infected_upper\": 166621.4285714286, \"n_days\": 86, \"infected\": 116635.0}, {\"index\": 10637, \"location\": \"Iran\", \"date\": \"2020-05-08T00:00:00\", \"total_infected\": 118392.0, \"total_infected_lower\": 104691.0, \"total_infected_upper\": 169131.42857142858, \"n_days\": 87, \"infected\": 118392.0}, {\"index\": 10638, \"location\": \"Iran\", \"date\": \"2020-05-09T00:00:00\", \"total_infected\": 120198.0, \"total_infected_lower\": 106220.0, \"total_infected_upper\": 171711.42857142858, \"n_days\": 88, \"infected\": 120198.0}, {\"index\": 10639, \"location\": \"Iran\", \"date\": \"2020-05-10T00:00:00\", \"total_infected\": 122492.0, \"total_infected_lower\": 107603.0, \"total_infected_upper\": 174988.57142857142, \"n_days\": 89, \"infected\": 122492.0}, {\"index\": 10640, \"location\": \"Iran\", \"date\": \"2020-05-11T00:00:00\", \"total_infected\": 124603.0, \"total_infected_lower\": 109286.0, \"total_infected_upper\": 178004.2857142857, \"n_days\": 90, \"infected\": 124603.0}, {\"index\": 10641, \"location\": \"Iran\", \"date\": \"2020-05-12T00:00:00\", \"total_infected\": 126949.0, \"total_infected_lower\": 110767.0, \"total_infected_upper\": 181355.71428571432, \"n_days\": 91, \"infected\": 126949.0}, {\"index\": 10642, \"location\": \"Iran\", \"date\": \"2020-05-13T00:00:00\", \"total_infected\": 129341.0, \"total_infected_lower\": 112725.0, \"total_infected_upper\": 184772.85714285716, \"n_days\": 92, \"infected\": 129341.0}, {\"index\": 10643, \"location\": \"Iran\", \"date\": \"2020-05-14T00:00:00\", \"total_infected\": 131652.0, \"total_infected_lower\": 114533.0, \"total_infected_upper\": 188074.28571428574, \"n_days\": 93, \"infected\": 131652.0}, {\"index\": 10644, \"location\": \"Iran\", \"date\": \"2020-05-15T00:00:00\", \"total_infected\": 133521.0, \"total_infected_lower\": 116635.0, \"total_infected_upper\": 190744.2857142857, \"n_days\": 94, \"infected\": 133521.0}, {\"index\": 10645, \"location\": \"Iran\", \"date\": \"2020-05-16T00:00:00\", \"total_infected\": 135701.0, \"total_infected_lower\": 118392.0, \"total_infected_upper\": 193858.57142857145, \"n_days\": 95, \"infected\": 135701.0}, {\"index\": 10646, \"location\": \"Iran\", \"date\": \"2020-05-17T00:00:00\", \"total_infected\": 137724.0, \"total_infected_lower\": 120198.0, \"total_infected_upper\": 196748.57142857142, \"n_days\": 96, \"infected\": 137724.0}, {\"index\": 10647, \"location\": \"Iran\", \"date\": \"2020-05-18T00:00:00\", \"total_infected\": 139511.0, \"total_infected_lower\": 122492.0, \"total_infected_upper\": 199301.4285714286, \"n_days\": 97, \"infected\": 139511.0}, {\"index\": 10648, \"location\": \"Iran\", \"date\": \"2020-05-19T00:00:00\", \"total_infected\": 141591.0, \"total_infected_lower\": 124603.0, \"total_infected_upper\": 202272.85714285716, \"n_days\": 98, \"infected\": 141591.0}, {\"index\": 10649, \"location\": \"Iran\", \"date\": \"2020-05-20T00:00:00\", \"total_infected\": 143849.0, \"total_infected_lower\": 126949.0, \"total_infected_upper\": 205498.57142857142, \"n_days\": 99, \"infected\": 143849.0}, {\"index\": 10650, \"location\": \"Iran\", \"date\": \"2020-05-21T00:00:00\", \"total_infected\": 146668.0, \"total_infected_lower\": 129341.0, \"total_infected_upper\": 209525.71428571432, \"n_days\": 100, \"infected\": 146668.0}, {\"index\": 10651, \"location\": \"Iran\", \"date\": \"2020-05-22T00:00:00\", \"total_infected\": 148950.0, \"total_infected_lower\": 131652.0, \"total_infected_upper\": 212785.7142857143, \"n_days\": 101, \"infected\": 148950.0}, {\"index\": 10652, \"location\": \"Iran\", \"date\": \"2020-05-23T00:00:00\", \"total_infected\": 151387.83590116102, \"total_infected_lower\": 133521.0, \"total_infected_upper\": 216268.33700165857, \"n_days\": 102, \"infected\": 151387.83590116102}, {\"index\": 10653, \"location\": \"Iran\", \"date\": \"2020-05-24T00:00:00\", \"total_infected\": 153865.57139198962, \"total_infected_lower\": 135701.0, \"total_infected_upper\": 219807.95913141372, \"n_days\": 103, \"infected\": 153865.57139198962}, {\"index\": 10654, \"location\": \"Iran\", \"date\": \"2020-05-25T00:00:00\", \"total_infected\": 156383.8595013689, \"total_infected_lower\": 137724.0, \"total_infected_upper\": 223405.51357338412, \"n_days\": 104, \"infected\": 156383.8595013689}, {\"index\": 10655, \"location\": \"Iran\", \"date\": \"2020-05-26T00:00:00\", \"total_infected\": 158943.3639461796, \"total_infected_lower\": 139511.0, \"total_infected_upper\": 227061.94849454227, \"n_days\": 105, \"infected\": 158943.3639461796}, {\"index\": 10656, \"location\": \"Iran\", \"date\": \"2020-05-27T00:00:00\", \"total_infected\": 161544.75930622857, \"total_infected_lower\": 141591.0, \"total_infected_upper\": 230778.22758032652, \"n_days\": 106, \"infected\": 161544.75930622857}, {\"index\": 10657, \"location\": \"Iran\", \"date\": \"2020-05-28T00:00:00\", \"total_infected\": 164188.73120204016, \"total_infected_lower\": 143849.0, \"total_infected_upper\": 234555.3302886288, \"n_days\": 107, \"infected\": 164188.73120204016}, {\"index\": 10658, \"location\": \"Iran\", \"date\": \"2020-05-29T00:00:00\", \"total_infected\": 166875.9764755575, \"total_infected_lower\": 146668.0, \"total_infected_upper\": 238394.25210793928, \"n_days\": 108, \"infected\": 166875.9764755575}, {\"index\": 10659, \"location\": \"Iran\", \"date\": \"2020-05-30T00:00:00\", \"total_infected\": 169607.20337380128, \"total_infected_lower\": 148950.0, \"total_infected_upper\": 242296.00481971612, \"n_days\": 109, \"infected\": 169607.20337380128}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Plot estimated absolute number of infected\n",
    "plot1 = plot(data_countries_pc, \"line\", True)\n",
    "#save(plot1, \"../images/covid-estimate-infections.png\")\n",
    "plot1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017732,
     "end_time": "2020-04-28T12:14:02.693260",
     "exception": false,
     "start_time": "2020-04-28T12:14:02.675528",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Latest Country Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-28T12:14:02.753310Z",
     "iopub.status.busy": "2020-04-28T12:14:02.750590Z",
     "iopub.status.idle": "2020-04-28T12:14:02.770091Z",
     "shell.execute_reply": "2020-04-28T12:14:02.770853Z"
    },
    "papermill": {
     "duration": 0.061589,
     "end_time": "2020-04-28T12:14:02.771023",
     "exception": false,
     "start_time": "2020-04-28T12:14:02.709434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Estimated Infected</th>\n",
       "      <th>Lower Bound Estimates</th>\n",
       "      <th>Upper Bound Estimates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>797,753</td>\n",
       "      <td>613,656</td>\n",
       "      <td>1,139,648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>84,178</td>\n",
       "      <td>84,128</td>\n",
       "      <td>120,254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>201,090</td>\n",
       "      <td>188,752</td>\n",
       "      <td>287,271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>187,227</td>\n",
       "      <td>183,189</td>\n",
       "      <td>267,467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>169,607</td>\n",
       "      <td>148,950</td>\n",
       "      <td>242,296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>236,935</td>\n",
       "      <td>232,664</td>\n",
       "      <td>338,479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>16,903</td>\n",
       "      <td>16,716</td>\n",
       "      <td>24,148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>34,723</td>\n",
       "      <td>32,203</td>\n",
       "      <td>49,604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>38,764</td>\n",
       "      <td>34,366</td>\n",
       "      <td>55,377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>11,884</td>\n",
       "      <td>11,468</td>\n",
       "      <td>16,977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>245,269</td>\n",
       "      <td>239,228</td>\n",
       "      <td>350,384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>290,061</td>\n",
       "      <td>274,219</td>\n",
       "      <td>414,373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>1,961,836</td>\n",
       "      <td>1,770,165</td>\n",
       "      <td>2,802,623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Date Estimated Infected Lower Bound Estimates  \\\n",
       "Country                                                              \n",
       "Brazil         2020-05-30            797,753               613,656   \n",
       "China          2020-05-30             84,178                84,128   \n",
       "France         2020-05-30            201,090               188,752   \n",
       "Germany        2020-05-30            187,227               183,189   \n",
       "Iran           2020-05-30            169,607               148,950   \n",
       "Italy          2020-05-30            236,935               232,664   \n",
       "Japan          2020-05-30             16,903                16,716   \n",
       "Portugal       2020-05-30             34,723                32,203   \n",
       "Singapore      2020-05-30             38,764                34,366   \n",
       "South Korea    2020-05-30             11,884                11,468   \n",
       "Spain          2020-05-30            245,269               239,228   \n",
       "United Kingdom 2020-05-30            290,061               274,219   \n",
       "United States  2020-05-30          1,961,836             1,770,165   \n",
       "\n",
       "               Upper Bound Estimates  \n",
       "Country                               \n",
       "Brazil                     1,139,648  \n",
       "China                        120,254  \n",
       "France                       287,271  \n",
       "Germany                      267,467  \n",
       "Iran                         242,296  \n",
       "Italy                        338,479  \n",
       "Japan                         24,148  \n",
       "Portugal                      49,604  \n",
       "Singapore                     55,377  \n",
       "South Korea                   16,977  \n",
       "Spain                        350,384  \n",
       "United Kingdom               414,373  \n",
       "United States              2,802,623  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input    \n",
    "label = 'Estimated Infected'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label}'\n",
    "temp.columns = ['Country', 'Date', metric_name, \"Lower Bound Estimates\", \"Upper Bound Estimates\"]\n",
    "temp.loc[:, \"Estimated Infected\"] = temp.loc[:, \"Estimated Infected\"].round(0).map('{:,.0f}'.format) \n",
    "temp.loc[:, \"Lower Bound Estimates\"] = temp.loc[:, \"Lower Bound Estimates\"].round(0).map('{:,.0f}'.format) \n",
    "temp.loc[:, \"Upper Bound Estimates\"] = temp.loc[:, \"Upper Bound Estimates\"].round(0).map('{:,.0f}'.format) \n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016578,
     "end_time": "2020-04-28T12:14:02.804390",
     "exception": false,
     "start_time": "2020-04-28T12:14:02.787812",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Infected vs. number of confirmed cases\n",
    "> Allows you to compare how countries have been tracking the true number of infected people. The smaller deviation from the dashed line (45 degree line) the better job at tracking the true number of infected people."
   ]
  },
  {
   "cell_type": "markdown",
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     "duration": 0.016459,
     "end_time": "2020-04-28T12:14:02.837402",
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     "start_time": "2020-04-28T12:14:02.820943",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2020-04-28T12:14:03.168618Z",
     "shell.execute_reply": "2020-04-28T12:14:03.169114Z"
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     "exception": false,
     "start_time": "2020-04-28T12:14:02.854153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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\"infected\": 95646.0, \"45_line\": 87026}, {\"index\": 10623, \"location\": \"Iran\", \"date\": \"2020-04-24T00:00:00\", \"total_cases\": 88194, \"total_infected\": 96448.0, \"n_days\": 73, \"cases\": 88194, \"infected\": 96448.0, \"45_line\": 88194}, {\"index\": 10624, \"location\": \"Iran\", \"date\": \"2020-04-25T00:00:00\", \"total_cases\": 89328, \"total_infected\": 97424.0, \"n_days\": 74, \"cases\": 89328, \"infected\": 97424.0, \"45_line\": 89328}, {\"index\": 10625, \"location\": \"Iran\", \"date\": \"2020-04-26T00:00:00\", \"total_cases\": 90481, \"total_infected\": 98647.0, \"n_days\": 75, \"cases\": 90481, \"infected\": 98647.0, \"45_line\": 90481}, {\"index\": 10626, \"location\": \"Iran\", \"date\": \"2020-04-27T00:00:00\", \"total_cases\": 91472, \"total_infected\": 99970.0, \"n_days\": 76, \"cases\": 91472, \"infected\": 99970.0, \"45_line\": 91472}, {\"index\": 10627, \"location\": \"Iran\", \"date\": \"2020-04-28T00:00:00\", \"total_cases\": 92584, \"total_infected\": 101650.0, \"n_days\": 77, \"cases\": 92584, \"infected\": 101650.0, \"45_line\": 92584}, {\"index\": 10628, \"location\": \"Iran\", \"date\": \"2020-04-29T00:00:00\", \"total_cases\": 93657, \"total_infected\": 103134.99999999999, \"n_days\": 78, \"cases\": 93657, \"infected\": 103134.99999999999, \"45_line\": 93657}, {\"index\": 10629, \"location\": \"Iran\", \"date\": \"2020-04-30T00:00:00\", \"total_cases\": 94640, \"total_infected\": 104691.0, \"n_days\": 79, \"cases\": 94640, \"infected\": 104691.0, \"45_line\": 94640}, {\"index\": 10630, \"location\": \"Iran\", \"date\": \"2020-05-01T00:00:00\", \"total_cases\": 95646, \"total_infected\": 106220.0, \"n_days\": 80, \"cases\": 95646, \"infected\": 106220.0, \"45_line\": 95646}, {\"index\": 10631, \"location\": \"Iran\", \"date\": \"2020-05-02T00:00:00\", \"total_cases\": 96448, \"total_infected\": 107603.0, \"n_days\": 81, \"cases\": 96448, \"infected\": 107603.0, \"45_line\": 96448}, {\"index\": 10632, \"location\": \"Iran\", \"date\": \"2020-05-03T00:00:00\", \"total_cases\": 97424, \"total_infected\": 109286.0, \"n_days\": 82, \"cases\": 97424, \"infected\": 109286.0, \"45_line\": 97424}, {\"index\": 10633, \"location\": \"Iran\", \"date\": \"2020-05-04T00:00:00\", \"total_cases\": 98647, \"total_infected\": 110767.0, \"n_days\": 83, \"cases\": 98647, \"infected\": 110767.0, \"45_line\": 98647}, {\"index\": 10634, \"location\": \"Iran\", \"date\": \"2020-05-05T00:00:00\", \"total_cases\": 99970, \"total_infected\": 112725.0, \"n_days\": 84, \"cases\": 99970, \"infected\": 112725.0, \"45_line\": 99970}, {\"index\": 10635, \"location\": \"Iran\", \"date\": \"2020-05-06T00:00:00\", \"total_cases\": 101650, \"total_infected\": 114533.0, \"n_days\": 85, \"cases\": 101650, \"infected\": 114533.0, \"45_line\": 101650}, {\"index\": 10636, \"location\": \"Iran\", \"date\": \"2020-05-07T00:00:00\", \"total_cases\": 103135, \"total_infected\": 116635.0, \"n_days\": 86, \"cases\": 103135, \"infected\": 116635.0, \"45_line\": 103135}, {\"index\": 10637, \"location\": \"Iran\", \"date\": \"2020-05-08T00:00:00\", \"total_cases\": 104691, \"total_infected\": 118392.0, \"n_days\": 87, \"cases\": 104691, \"infected\": 118392.0, \"45_line\": 104691}, {\"index\": 10638, \"location\": \"Iran\", \"date\": \"2020-05-09T00:00:00\", \"total_cases\": 106220, \"total_infected\": 120198.0, \"n_days\": 88, \"cases\": 106220, \"infected\": 120198.0, \"45_line\": 106220}, {\"index\": 10639, \"location\": \"Iran\", \"date\": \"2020-05-10T00:00:00\", \"total_cases\": 107603, \"total_infected\": 122492.0, \"n_days\": 89, \"cases\": 107603, \"infected\": 122492.0, \"45_line\": 107603}, {\"index\": 10640, \"location\": \"Iran\", \"date\": \"2020-05-11T00:00:00\", \"total_cases\": 109286, \"total_infected\": 124603.0, \"n_days\": 90, \"cases\": 109286, \"infected\": 124603.0, \"45_line\": 109286}, {\"index\": 10641, \"location\": \"Iran\", \"date\": \"2020-05-12T00:00:00\", \"total_cases\": 110767, \"total_infected\": 126949.0, \"n_days\": 91, \"cases\": 110767, \"infected\": 126949.0, \"45_line\": 110767}, {\"index\": 10642, \"location\": \"Iran\", \"date\": \"2020-05-13T00:00:00\", \"total_cases\": 112725, \"total_infected\": 129341.0, \"n_days\": 92, \"cases\": 112725, \"infected\": 129341.0, \"45_line\": 112725}, {\"index\": 10643, \"location\": \"Iran\", \"date\": \"2020-05-14T00:00:00\", \"total_cases\": 114533, \"total_infected\": 131652.0, \"n_days\": 93, \"cases\": 114533, \"infected\": 131652.0, \"45_line\": 114533}, {\"index\": 10644, \"location\": \"Iran\", \"date\": \"2020-05-15T00:00:00\", \"total_cases\": 116635, \"total_infected\": 133521.0, \"n_days\": 94, \"cases\": 116635, \"infected\": 133521.0, \"45_line\": 116635}, {\"index\": 10645, \"location\": \"Iran\", \"date\": \"2020-05-16T00:00:00\", \"total_cases\": 118392, \"total_infected\": 135701.0, \"n_days\": 95, \"cases\": 118392, \"infected\": 135701.0, \"45_line\": 118392}, {\"index\": 10646, \"location\": \"Iran\", \"date\": \"2020-05-17T00:00:00\", \"total_cases\": 120198, \"total_infected\": 137724.0, \"n_days\": 96, \"cases\": 120198, \"infected\": 137724.0, \"45_line\": 120198}, {\"index\": 10647, \"location\": \"Iran\", \"date\": \"2020-05-18T00:00:00\", \"total_cases\": 122492, \"total_infected\": 139511.0, \"n_days\": 97, \"cases\": 122492, \"infected\": 139511.0, \"45_line\": 122492}, {\"index\": 10648, \"location\": \"Iran\", \"date\": \"2020-05-19T00:00:00\", \"total_cases\": 124603, \"total_infected\": 141591.0, \"n_days\": 98, \"cases\": 124603, \"infected\": 141591.0, \"45_line\": 124603}, {\"index\": 10649, \"location\": \"Iran\", \"date\": \"2020-05-20T00:00:00\", \"total_cases\": 126949, \"total_infected\": 143849.0, \"n_days\": 99, \"cases\": 126949, \"infected\": 143849.0, \"45_line\": 126949}, {\"index\": 10650, \"location\": \"Iran\", \"date\": \"2020-05-21T00:00:00\", \"total_cases\": 129341, \"total_infected\": 146668.0, \"n_days\": 100, \"cases\": 129341, \"infected\": 146668.0, \"45_line\": 129341}, {\"index\": 10651, \"location\": \"Iran\", \"date\": \"2020-05-22T00:00:00\", \"total_cases\": 131652, \"total_infected\": 148950.0, \"n_days\": 101, \"cases\": 131652, \"infected\": 148950.0, \"45_line\": 131652}, {\"index\": 10652, \"location\": \"Iran\", \"date\": \"2020-05-23T00:00:00\", \"total_cases\": 133521, \"total_infected\": 151387.83590116102, \"n_days\": 102, \"cases\": 133521, \"infected\": 151387.83590116102, \"45_line\": 133521}, {\"index\": 10653, \"location\": \"Iran\", \"date\": \"2020-05-24T00:00:00\", \"total_cases\": 135701, \"total_infected\": 153865.57139198962, \"n_days\": 103, \"cases\": 135701, \"infected\": 153865.57139198962, \"45_line\": 135701}, {\"index\": 10654, \"location\": \"Iran\", \"date\": \"2020-05-25T00:00:00\", \"total_cases\": 137724, \"total_infected\": 156383.8595013689, \"n_days\": 104, \"cases\": 137724, \"infected\": 156383.8595013689, \"45_line\": 137724}, {\"index\": 10655, \"location\": \"Iran\", \"date\": \"2020-05-26T00:00:00\", \"total_cases\": 139511, \"total_infected\": 158943.3639461796, \"n_days\": 105, \"cases\": 139511, \"infected\": 158943.3639461796, \"45_line\": 139511}, {\"index\": 10656, \"location\": \"Iran\", \"date\": \"2020-05-27T00:00:00\", \"total_cases\": 141591, \"total_infected\": 161544.75930622857, \"n_days\": 106, \"cases\": 141591, \"infected\": 161544.75930622857, \"45_line\": 141591}, {\"index\": 10657, \"location\": \"Iran\", \"date\": \"2020-05-28T00:00:00\", \"total_cases\": 143849, \"total_infected\": 164188.73120204016, \"n_days\": 107, \"cases\": 143849, \"infected\": 164188.73120204016, \"45_line\": 143849}, {\"index\": 10658, \"location\": \"Iran\", \"date\": \"2020-05-29T00:00:00\", \"total_cases\": 146668, \"total_infected\": 166875.9764755575, \"n_days\": 108, \"cases\": 146668, \"infected\": 166875.9764755575, \"45_line\": 146668}, {\"index\": 10659, \"location\": \"Iran\", \"date\": \"2020-05-30T00:00:00\", \"total_cases\": 148950, \"total_infected\": 169607.20337380128, \"n_days\": 109, \"cases\": 148950, \"infected\": 169607.20337380128, \"45_line\": 148950}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Plot it using Altair\n",
    "data_pc = data_final[['location', 'date', 'total_cases', 'total_infected']].copy()\n",
    "\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\"]\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_infected\"] = data_pc.loc[data_pc.location == i,\"total_infected\"]\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]\n",
    "    # get each country time series\n",
    "filter1 = data_pc[\"total_infected\"] > 1\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])\n",
    "\n",
    "\n",
    "plot(data_countries_pc, \"scatter\", True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.022083,
     "end_time": "2020-04-28T12:14:03.212166",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.190083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Latest Observed vs. Estimate of Infected Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-28T12:14:03.262506Z",
     "iopub.status.busy": "2020-04-28T12:14:03.261149Z",
     "iopub.status.idle": "2020-04-28T12:14:03.288117Z",
     "shell.execute_reply": "2020-04-28T12:14:03.287500Z"
    },
    "papermill": {
     "duration": 0.055663,
     "end_time": "2020-04-28T12:14:03.288232",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.232569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Observed Cases</th>\n",
       "      <th>Estimated Infected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>498,440</td>\n",
       "      <td>797,753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>84,128</td>\n",
       "      <td>84,178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>188,752</td>\n",
       "      <td>201,090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>183,189</td>\n",
       "      <td>187,227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>148,950</td>\n",
       "      <td>169,607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>232,664</td>\n",
       "      <td>236,935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>16,716</td>\n",
       "      <td>16,903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>32,203</td>\n",
       "      <td>34,723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>34,366</td>\n",
       "      <td>38,764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>11,468</td>\n",
       "      <td>11,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>239,228</td>\n",
       "      <td>245,269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>274,219</td>\n",
       "      <td>290,061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>1,770,165</td>\n",
       "      <td>1,961,836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Date Observed Cases Estimated Infected\n",
       "Country                                                    \n",
       "Brazil         2020-05-30        498,440            797,753\n",
       "China          2020-05-30         84,128             84,178\n",
       "France         2020-05-30        188,752            201,090\n",
       "Germany        2020-05-30        183,189            187,227\n",
       "Iran           2020-05-30        148,950            169,607\n",
       "Italy          2020-05-30        232,664            236,935\n",
       "Japan          2020-05-30         16,716             16,903\n",
       "Portugal       2020-05-30         32,203             34,723\n",
       "Singapore      2020-05-30         34,366             38,764\n",
       "South Korea    2020-05-30         11,468             11,884\n",
       "Spain          2020-05-30        239,228            245,269\n",
       "United Kingdom 2020-05-30        274,219            290,061\n",
       "United States  2020-05-30      1,770,165          1,961,836"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label1 = 'Observed Cases'\n",
    "label2 = 'Estimated Infected'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name1 = f'{label1}'\n",
    "metric_name2 = f'{label2}'\n",
    "temp.columns = ['Country', 'Date', metric_name1, metric_name2]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, \"Observed Cases\"] = temp.loc[:, \"Observed Cases\"].round(0).map('{:,.0f}'.format)\n",
    "temp.loc[:, \"Estimated Infected\"] = temp.loc[:, \"Estimated Infected\"].round(0).map('{:,.0f}'.format)\n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020615,
     "end_time": "2020-04-28T12:14:03.330085",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.309470",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Methodology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020571,
     "end_time": "2020-04-28T12:14:03.371195",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.350624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We argue that the number of infected in the past can be inferred using today's number of deaths and average fatality rate from confirmed cases in the following way:\n",
    "\n",
    "{%raw%}$$I_{t-j} = \\frac{D_t}{{CFR}_t}$${%endraw%}\n",
    "\n",
    "where {% raw %}$I_t${% endraw %} = number of infected, {% raw %}$D_t${% endraw %} = number of deaths, and {% raw %}${CFR}_t ${% endraw %} = case fatality rate = {% raw %}$\\frac{D}{C}${% endraw %}. The {% raw %}$j${% endraw %} depends on the average number of days that covid patients die after having the first symptoms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.02045,
     "end_time": "2020-04-28T12:14:03.412258",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.391808",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Assumption 1**: The case fatality rate is a good proxy for the fatality rate of the infected population\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020502,
     "end_time": "2020-04-28T12:14:03.453308",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.432806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then, in order to estimate the current number of infected {% raw %}$I_t${% endraw %} we need to estimate its growth rate from {% raw %}$t-j${% endraw %} to {% raw %}$t${% endraw %}.\n",
    "\n",
    "{% raw %}$$I_t = (1+\\hat{g})^j I_{t-j}$${% endraw %}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020362,
     "end_time": "2020-04-28T12:14:03.494129",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.473767",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Assumption 2**: The growth rate of infected $\\hat{g}$ is an unbiased estimate of $g$ .\n",
    "\n",
    "For now we estimate $g$ using the average growth rate since having the first infected person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021119,
     "end_time": "2020-04-28T12:14:03.535953",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.514834",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Assumption 3**: It takes on average 8 days to die after having the first symptoms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021093,
     "end_time": "2020-04-28T12:14:03.577685",
     "exception": false,
     "start_time": "2020-04-28T12:14:03.556592",
     "status": "completed"
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   },
   "source": [
    "This analysis was conducted by [Joao B. Duarte](http://jbduarte.com). Relevant sources are listed below: \n",
    "\n",
    "\n",
    "1. [2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE](https://systems.jhu.edu/research/public-health/ncov/) [GitHub repository](https://github.com/CSSEGISandData/COVID-19). \n",
    "\n",
    "2. [Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), \"The Next Generation of the Penn World Table\" American Economic Review, 105(10), 3150-3182](https://www.rug.nl/ggdc/productivity/pwt/related-research)\n"
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